{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([[11, 12, 13, 14, 15],\n",
    "              [16, 17, 18, 19, 20],\n",
    "              [21, 22, 23, 24, 25],\n",
    "              [26, 27, 28 ,29, 30],\n",
    "              [31, 32, 33, 34, 35]])\n",
    "\n",
    "a.shape\n",
    "\n",
    "print(a[2,4]) # >>>25\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xbb51820>,\n",
       " <matplotlib.lines.Line2D at 0xbb62040>,\n",
       " <matplotlib.lines.Line2D at 0xbb62298>,\n",
       " <matplotlib.lines.Line2D at 0xbb62160>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(2,1)\n",
    "plt.subplot(2,1,1)\n",
    "\n",
    "x=np.linspace(-10,10,100)\n",
    "y=np.sin(x)\n",
    "\n",
    "plt.plot(x,y, marker='o')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "\n",
    "a = np.arange(10)\n",
    "plt.plot(a, a * 1.5, a, a*2.5, a, a* 3.5, a, a*4.5)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a6dc62afd8b03c17538a9dfce2fcb18f62cec380cc7b77050462a64b7e4e4814"
  },
  "kernelspec": {
   "display_name": "Python 3.8.0 32-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
